{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#人数(单位：万人)\n",
    "population=[20.55,22.44,25.37,27.13,29.45,30.10,30.96,34.06,36.42,38.09,39.13,39.99,41.93,44.59,47.30,52.89,55.73,56.76,59.17,60.63]\n",
    "#机动车数(单位：万辆)\n",
    "vehicle=[0.6,0.75,0.85,0.9,1.05,1.35,1.45,1.6,1.7,1.85,2.15,2.2,2.25,2.35,2.5,2.6,2.7,2.85,2.95,3.1]\n",
    "#公路面积(单位：万平方公里)\n",
    "roadarea=[0.09,0.11,0.11,0.14,0.20,0.23,0.23,0.32,0.32,0.34,0.36,0.36,0.38,0.49,0.56,0.59,0.59,0.67,0.69,0.79]\n",
    "#公路客运量(单位：万人)\n",
    "passengertraffic=[5126,6217,7730,9145,10460,11387,12353,15750,18304,19836,21024,19490,20433,22598,25107,33442,36836,40548,42927,43462]\n",
    "#公路货运量(单位：万吨)\n",
    "freighttraffic=[1237,1379,1385,1399,1663,1714,1834,4322,8132,8936,11099,11203,10524,11115,13320,16762,18673,20724,20803,21804]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "samplein = np.mat([population,vehicle,roadarea]) \n",
    "'''\n",
    "[[ 20.55  22.44  25.37  27.13  29.45  30.1   30.96  34.06  36.42  38.09\n",
    "   39.13  39.99  41.93  44.59  47.3   52.89  55.73  56.76  59.17  60.63]\n",
    " [  0.6    0.75   0.85   0.9    1.05   1.35   1.45   1.6    1.7    1.85\n",
    "    2.15   2.2    2.25   2.35   2.5    2.6    2.7    2.85   2.95   3.1 ]\n",
    " [  0.09   0.11   0.11   0.14   0.2    0.23   0.23   0.32   0.32   0.34\n",
    "    0.36   0.36   0.38   0.49   0.56   0.59   0.59   0.67   0.69   0.79]]\n",
    "'''\n",
    "sampleinminmax = np.array([samplein.min(axis=1).T.tolist()[0],samplein.max(axis=1).T.tolist()[0]]).transpose()#3*2，对应最大值最小值\n",
    "'''\n",
    "[[ 20.55  60.63]\n",
    " [  0.6    3.1 ]\n",
    " [  0.09   0.79]]\n",
    "'''\n",
    "sampleout = np.mat([passengertraffic,freighttraffic])#2*20\n",
    "sampleoutminmax = np.array([sampleout.min(axis=1).T.tolist()[0],sampleout.max(axis=1).T.tolist()[0]]).transpose()#2*2，对应最大值最小值\n",
    "\n",
    "#标准化\n",
    "#3*20\n",
    "sampleinnorm = (2*(np.array(samplein.T)-sampleinminmax.T[0])/(sampleinminmax.T[1]-sampleinminmax.T[0])-1).transpose()\n",
    "'''\n",
    "[[-1.         -0.90568862 -0.75948104 -0.67165669 -0.55588822 -0.52345309\n",
    "  -0.48053892 -0.3258483  -0.20808383 -0.1247505  -0.07285429 -0.02994012\n",
    "   0.06686627  0.1996008   0.33483034  0.61377246  0.75548902  0.80688623\n",
    "   0.92714571  1.        ]\n",
    " [-1.         -0.88       -0.8        -0.76       -0.64       -0.4        -0.32\n",
    "  -0.2        -0.12        0.          0.24        0.28        0.32        0.4\n",
    "   0.52        0.6         0.68        0.8         0.88        1.        ]\n",
    " [-1.         -0.94285714 -0.94285714 -0.85714286 -0.68571429 -0.6        -0.6\n",
    "  -0.34285714 -0.34285714 -0.28571429 -0.22857143 -0.22857143 -0.17142857\n",
    "   0.14285714  0.34285714  0.42857143  0.42857143  0.65714286  0.71428571\n",
    "   1.        ]]\n",
    "'''\n",
    "#2*20\n",
    "sampleoutnorm = (2*(np.array(sampleout.T)-sampleoutminmax.T[0])/(sampleoutminmax.T[1]-sampleoutminmax.T[0])-1).transpose()\n",
    "\n",
    "#给输出样本添加噪音\n",
    "noise = 0.03*np.random.rand(sampleoutnorm.shape[0],sampleoutnorm.shape[1])\n",
    "sampleoutnorm += noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#超参数\n",
    "maxepochs = 60000\n",
    "learnrate = 0.035\n",
    "errorfinal = 0.65*10**(-3)\n",
    "samnum = 20\n",
    "indim = 3\n",
    "outdim = 2\n",
    "hiddenunitnum = 8\n",
    "\n",
    "# 网络设计\n",
    "w1 = 0.5*np.random.rand(hiddenunitnum,indim)-0.1\n",
    "b1 = 0.5*np.random.rand(hiddenunitnum,1)-0.1\n",
    "w2 = 0.5*np.random.rand(outdim,hiddenunitnum)-0.1\n",
    "b2 = 0.5*np.random.rand(outdim,1)-0.1\n",
    "\n",
    "def logsig(x):\n",
    "    return 1/(1+np.exp(-x))\n",
    "\n",
    "errhistory = []\n",
    "# BP算法遍历\n",
    "for i in range(maxepochs):\n",
    "    #隐藏层输出\n",
    "    hiddenout = logsig((np.dot(w1,sampleinnorm).transpose()+b1.transpose())).transpose()\n",
    "    # 输出层输出\n",
    "    networkout = (np.dot(w2,hiddenout).transpose()+b2.transpose()).transpose()\n",
    "    # 错误\n",
    "    err = sampleoutnorm - networkout\n",
    "    sse = sum(sum(err**2))\n",
    "\n",
    "    errhistory.append(sse)\n",
    "    if sse < errorfinal:\n",
    "        break\n",
    "\n",
    "    delta2 = err\n",
    "\n",
    "    delta1 = np.dot(w2.transpose(),delta2)*hiddenout*(1-hiddenout)\n",
    "\n",
    "    dw2 = np.dot(delta2,hiddenout.transpose())\n",
    "    db2 = np.dot(delta2,np.ones((samnum,1)))\n",
    "\n",
    "    dw1 = np.dot(delta1,sampleinnorm.transpose())\n",
    "    db1 = np.dot(delta1,np.ones((samnum,1)))\n",
    "\n",
    "    w2 += learnrate*dw2\n",
    "    b2 += learnrate*db2\n",
    "\n",
    "    w1 += learnrate*dw1\n",
    "    b1 += learnrate*db1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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FF5ezTVDOIj372bDTTrDjjuVr5/eTJpXhPElqNwYjSV3aeONyj6R99lnZllke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\nRpIkSRWDkSRJUsVgJEmSVPn/XDJzCFT5qOsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11862aa36d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 误差曲线图\n",
    "errhistory10 = np.log10(errhistory)\n",
    "minerr = min(errhistory10)\n",
    "plt.plot(errhistory10)\n",
    "plt.plot(range(0,i+1000,1000),[minerr]*len(range(0,i+1000,1000)))\n",
    "\n",
    "ax=plt.gca()\n",
    "ax.set_yticks([-2,-1,0,1,2,minerr])\n",
    "ax.set_yticklabels([u'$10^{-2}$',u'$10^{-1}$',u'$1$',u'$10^{1}$',u'$10^{2}$',str(('%.4f'%np.power(10,minerr)))])\n",
    "ax.set_xlabel('iteration')\n",
    "ax.set_ylabel('error')\n",
    "ax.set_title('Error History')\n",
    "#plt.savefig('errorhistory.png',dpi=700)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wzbyUU65tgMTExCJr+3TgnCvpEERERERE5C9CSXcuVq3x8d+5RTMdXZhtDx06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oN3vfnFwItmlu6ce9Vf50ZgArAO+D9/2e9BfWSP463AOGAOMBioDQwE2phZq6AvJxxQHvgMmAG8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\niCjpFhEREREREQmR/wfCqsHooHHPgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118616031d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 仿真输出和实际输出对比图\n",
    "hiddenout = logsig((np.dot(w1,sampleinnorm).transpose()+b1.transpose())).transpose()\n",
    "networkout = (np.dot(w2,hiddenout).transpose()+b2.transpose()).transpose()\n",
    "diff = sampleoutminmax[:,1]-sampleoutminmax[:,0]\n",
    "networkout2 = (networkout+1)/2\n",
    "networkout2[0] = networkout2[0]*diff[0]+sampleoutminmax[0][0]\n",
    "networkout2[1] = networkout2[1]*diff[1]+sampleoutminmax[1][0]\n",
    "\n",
    "sampleout = np.array(sampleout)\n",
    "\n",
    "fig,axes = plt.subplots(nrows=2,ncols=1,figsize=(12,10))\n",
    "line1, =axes[0].plot(networkout2[0],'k',marker = u'$\\circ$')\n",
    "line2, = axes[0].plot(sampleout[0],'r',markeredgecolor='b',marker = u'$\\star$',markersize=9)\n",
    "\n",
    "axes[0].legend((line1,line2),('simulation output','real output'),loc = 'upper left')\n",
    "\n",
    "yticks = [0,20000,40000,60000]\n",
    "ytickslabel = [u'$0$',u'$2$',u'$4$',u'$6$']\n",
    "axes[0].set_yticks(yticks)\n",
    "axes[0].set_yticklabels(ytickslabel)\n",
    "axes[0].set_ylabel(u'passenger traffic$(10^4)$')\n",
    "\n",
    "xticks = range(0,20,2)\n",
    "xtickslabel = range(1990,2010,2)\n",
    "axes[0].set_xticks(xticks)\n",
    "axes[0].set_xticklabels(xtickslabel)\n",
    "axes[0].set_xlabel(u'year')\n",
    "axes[0].set_title('Passenger Traffic Simulation')\n",
    "\n",
    "line3, = axes[1].plot(networkout2[1],'k',marker = u'$\\circ$')\n",
    "line4, = axes[1].plot(sampleout[1],'r',markeredgecolor='b',marker = u'$\\star$',markersize=9)\n",
    "axes[1].legend((line3,line4),('simulation output','real output'),loc = 'upper left')\n",
    "yticks = [0,10000,20000,30000]\n",
    "ytickslabel = [u'$0$',u'$1$',u'$2$',u'$3$']\n",
    "axes[1].set_yticks(yticks)\n",
    "axes[1].set_yticklabels(ytickslabel)\n",
    "axes[1].set_ylabel(u'freight traffic$(10^4)$')\n",
    "\n",
    "xticks = range(0,20,2)\n",
    "xtickslabel = range(1990,2010,2)\n",
    "axes[1].set_xticks(xticks)\n",
    "axes[1].set_xticklabels(xtickslabel)\n",
    "axes[1].set_xlabel(u'year')\n",
    "axes[1].set_title('Freight Traffic Simulation')\n",
    "\n",
    "#fig.savefig('simulation.png',dpi=500,bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}